China Tech Talk

This week, I welcomed Ben Norton onto my show. I have been following Ben for a while on his youtube channel and X account.

When I was in Beijing over the summer, I found out from some mutual contacts that Ben is in fact studying in Tsinghua University. I got in touch with him and we just couldn’t find a time to meet up. Fortunately, I was able to get Ben on my show this past week and it was great discussing with him about the topic of “moving to China and living in China”.

That’s one thought I had when I was in Beijing. I am now a part time resident here and it’s pretty good place to live in. We talked about living in China, moving to China and the pros & cons of China. There is certainly a lot of things we agreed on. We actually, chatted for a bit afterward too.

I hope I can meet up with Ben next summer when i’m there or if he ever visits the Northeast.

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This week, I welcomed back my friend Snek on the show to talk about something that will inevitably effect the entire global economy, but especially the tech sector, which is the bond meltdown and rising oil prices across the board.

I think we are at the point for many geopolitical reasons, foreign central banks are no longer price insensitive buyers of US Treasuries and this is part of what’s leading to surging yield globally. Of course, the surging bond issuance is another reason.

now for the AI buildout, surging bond yield is a huge problem since massive amount of issuance will need to happen. Oracle is having some real problem with that. Same with Softbank. It’s gotten so bad for Oracle that they gave Tencent a huge discount on that data center compute lease because Tencent is able to pay 30% up front. You know things are bad when that happens.

Let’s take Anthropic as an example. If you borrow $200B over a few years to pay for your data center build out and compute needs and you are facing almost junk bond ratings and 10 year yield is well over 5%. Then, your own yield could be close to 10% like it is getting there for softbank. 10% on $200B is $20B on interest per year. That changes the dynamic completely when it comes to margins on rest of your business.

So, what’s going on in the debt market really matters. We have been too used to record low interest rate. That has allowed many bad decisions and investment over time. Now, we are essentially getting back to a more historically sane interest rate, but US government is $40T in debt. Which at 5% yield means $2T in just interest payment per year. So, that is what we are dealing with here.

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I did a very early morning quick take on my trip to China. I’m sure there will be more on this since so much stuff happened while I was there. But I really wanted to just the message that China is becoming a really livable place and people are moving there for the quality of life. I met several people in Beijing this time who moved there from America from different reasons. It was interesting to hear about why they did it. I spoke about my own thoughts on what I enjoyed about living in Beijing and why I would consider moving there more permanently.

I think there are still many reasons to not move to China. For example, the intensity of life and work is much higher there than here. The competition for everything is so much higher and this drives people toward addictions that are all about the short term consumerism satisfaction like looking at micro drama on their phones and snacking/drinking milk tea all the time.

If I had to talk about what I think is problematic in Chinese society, the phone addiction and increased short termism seem to be the most obvious ones.

And those are also problems that likely will get worse with more competition and AI. But in many ways, the quality of live in Beijing is also just so much better than here.

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I had a chance to go over Tau Scaling and reusable rocket with Lei. As Lei is the person who shares my point of view the closest on Tau Scaling and Huawei’s efforts on this front, we really spent a lot of time explaining why what Huawei did in their design and production is quite special.

Even if transistor cell itself can continue to scale smaller, that does not apply to memory cell sizes or analog components like copper connections and such.

This episode was recorded before I went to China. Now that I’m back and Kirin 9050 has been unveiled, it’s a good time to publish my discussions on it.

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I am currently in Beijing and have the pleasure of getting together with some friends in Tsinghua University in the AI school. I was welcomed by Alex and Qian Yu. We sat down talked a lot about the academia, AI research in China, getting talent to come to China and future of AI both in LLM and embodied AI.

It’s quite the interesting experience to come here and see Zai in one corner, Unigroup building in another corner, the school for automation (using unreal new technology), Galbot robot booth, Infinigence, Vidu AI, Robotera and many other AI and semi startups.

Pay attention to the tips they have about the transitioning toward Ascend from Nvidia chips by researchers. Also, check the part about researchers in China finally finding Kimi K3 to be good enough for vibe coding compared to GPT and Claude. There are still some concerns about inference sped for Kimi. I think they need to get more compute.

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I was fortunate to be invited by Angelica Oung last week to talk about China’s AI progress in light of Kimi K3, DUVi lithography new, CXMT IPO and the KOSPI market crash.

Things have changed a little bit since the podcast, but CXMT remains very important, K3 remains the top Chinese model and lithography is still a big story.

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My quick thoughts on DSV4-Flash: Huge improvement over the original. It took 3 to 4 months, but we essentially got something close to GLM-5.2 in software engineering tasks.

I did some testing today on VS Code using cline plugin. DSv4-Flash is essentially free, so I’m going to keep it as a backup to GLM & Kimi’s latest models, since those coding plans are going to get used up quickly.

Going forward, I see 2 paths for open src/weight models:

* Smaller models that can run locally with some souped up hardware.

* Larger models that can need SuperNode to run and can compete with closed source flagship models.

All the developers are going to want to run these smaller LLMs locally. You don’t have to worry about AI company getting your data and can fine tune them anyway you want. This is going to drive an acceleration in “souped up” local hardware. Very bullish in my opinion for memory chips.

For the larger models, I think there really isn’t a lot of room for middle ground. You need to make them larger to compete fully with the Anthropic and OpenAI models.

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I’m staring a new series of what I call quick takes. These will be shorter (15-20 minutes) than scheduled podcast. They will be recorded whenever something interesting comes up in the tech world and I don’t want to wait to talk to someone about it.

So the first thing I did here is Kimi K3 and GLM-5.2 and what this tells us about China’s AI development. I think the overall story I want to put out there is the self improvement part of it and also models likely expanding over time and improved training data and flywheel with the Chinese labs.

By this point next year, we could very well (imo) see 5T+ parameter models coming out of China.

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This week, I had a chance to go on Carl Zha’s livestream to talk about the launch of Kimi K3 and what we saw at WAIC. Unfortunately, I had to leave early due to family obligations, but I think most of the issues I wanted to cover was already talked about. I’m actually a little surprised of the audio quality in there. I think next time, I have to go with something other than iPhone when I do this.

I just want again to point out something that I think is very under reported out there. Chinese models are getting larger and they will keep getting larger. Biren Tech reported this week that Chinese models will get to 5T+ range next year and it looks like we will reach 10T the year after as larger super node becomes available that can pool together all the high bandwidth memory and do efficient inference of these large MoE models. All of the major Chinese AI players like Moonshot, Zai, DeepSeek, Qwen and Minimax are going into new and larger sized models this year. Larger models require more compute to train, more data and more longer term optimization for inference. All of this has been made easier by the increased automation these firms can use to improve and speed up their own model development and training time. We are now seeing these very fast iterative improvement from Kimi and GLM in recent times. These are the new standards. The public is no longer happy with a new big improvement every 3 to 4 months. This needs to be every 1 to 2 months and eventually every month by the end of this year. Teams will need to have both flagship LLM and smaller and cheaper distilled models.

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This week, I invited Electric Felix on my show to talk more about EVs. I first encountered him on X/Twitter and then followed his journey on EVs in Europe as well as his trips in China and what he experienced there in Yunnan and charging stations. It was great talking to him and getting a sense of where Europe’s EV infrastructure and demand look like at the moment. I was in Spain and Italy earlier this year and the latter is still very hydrocarbon centric. We ended the talk with how the current trade dispute with Chinese EV industry in Europe gaining market share might get worked out. I think we will probably end up just seeing more localized production over time.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tphuang.substack.com